State-of-the-art natural language understanding systems, including spoken language understanding systems, aim to automatically identify the intent of the user and extract associated arguments (i.e., slots). The output of a natural language understanding system is typically normalized and interpreted into a structured query language or an application programming interface (API). Historically, intent determination is based from call classification systems (e.g., the AT&T “How May I Help You?” system) after the success of the early commercial interactive voice response (IVR) applications used in call centers. On the other hand, the slot filling task originated mostly from non-commercial projects such as the Airline Travel Information System (ATIS) project sponsored by the Defense Advanced Research Program Agency (DARPA).
TABLE 1A semantic template for the sample conversational input:“find me recent action movies with brad pitt”IntentSlotsEntities (Values)Find_MovieRelease_DaterecentGenreactionActorbrad pitt
These semantic template-based natural language understanding systems using intent determination and slot filling tasks rely on a semantic space, usually dictated by the target application. An example utterance with a corresponding semantic template is shown in Table 1. When statistical methods are employed, in-domain training data is collected and semantically annotated for model building and evaluation. The process of manually-annotating the training data is generally time-consuming and expensive. Further, semantic template-based natural language understanding systems and corresponding training methods do not scale well to the web, other domains, and other languages.
Previous efforts have used web search queries and search query click logs with the knowledge graph to bootstrap slot filling models in natural language understanding systems. Additionally, snippets returned from web search for pairs of related entities have been used to bootstrap intent detection models in order to catch previously unseen in-domain intents in natural language understanding systems. However, such supplemental efforts merely sought to improve slot filling and intent detection by aligning the semantic space of the natural language understanding system with the knowledge graph. The semantic space developed for a specific system is likely to have some semantic inconsistency with target knowledge stores, other dialog systems, and other semantic template-based systems that complicates mapping to knowledge sources and limits interoperability between systems.
It is with respect to these and other considerations that the present invention has been made. Although relatively specific problems have been discussed, it should be understood that the embodiments disclosed herein should not be limited to solving the specific problems identified in the background.